Blog

OpenAI's April 2026 outage took down API access for hours. Teams with automatic failover were unaffected. Here's how to build one that works before you need it.

A practical 15-point checklist for reducing AI API costs in 2026, ordered by savings impact and covering model selection, caching, routing, and compression.

Your monthly LLM bill is the wrong number to watch. AI cost per monthly active user is the metric that predicts whether your margins hold as you scale.

A complete breakdown of Anthropic Claude API pricing in 2026: cost scenarios for Haiku, Sonnet, Opus, and Fable 5, plus how prompt caching changes the math.

Azure OpenAI and OpenAI's direct API charge identical per-token rates. The real cost gap, up to 40%, is in everything else. Here's when each option wins.

GPT-4o-mini is 6.7x cheaper on input tokens than Claude Haiku 4.5. But cheaper per token isn't the same as cheaper per output. Here is when each one wins.

Fine-tuning is supposed to make models cheaper to run. In most cases, it costs more once training and engineering time are counted. Here's the break-even math.

Flash is exactly 2x cheaper than Pro at standard context lengths. Here is when that ratio matters, when it doesn't, and how to route without losing quality.

GPT-4o-mini handles 60-70% of production tasks with quality indistinguishable from GPT-4o. Here's the framework for knowing which side your workload falls on.

Running your entire product off one API key with no metadata tagging means you have a monthly AI bill with no idea where it comes from. Here's how to fix that.

Most teams track monthly LLM spend. That number tells you almost nothing about what to do next. Here are six metrics, like cache hit rate, that lead to action.

Six techniques, from prompt compression to output length control, that reduced one production prompt from 4,500 to 2,100 tokens. Here is how to apply them.

Every step in an agent loop compounds token count. A 20-step agent can cost 50x more than a single call. Here is the math and how to design agents on budget.

Combining model downgrade, caching, and prompt compression can cut an OpenAI bill by 60-80% with no visible quality loss. Six options, ordered by effort.

After OpenAI's 80% price cut on o3, the 'mini' model now costs more per token than the full model. This flips the usual reasoning-task routing logic entirely.

Prompt caching can cut up to 90% of your LLM input costs if your prompts repeat. Here is how it works on both providers, when it helps, and when it doesn't.

Most teams think of RAG costs as the LLM call. That's wrong. Context bloat alone can cost $18,000/month at moderate scale. Here are the four real cost centers.

A team spending $5,000/month on Claude can realistically reach $1,500-2,000/month without changing what their product does. Here's the 5-step practical guide.

Embedding costs are the most overlooked line item in AI infrastructure. At scale, even cheap models compound fast. Five techniques that cut costs 50-80%.

Most founding teams underestimate AI API costs by 5-10x in year one. Here are the rules of thumb, scale projections, and budget mistakes that will cost you.

AWS Bedrock gives you a managed AI platform inside AWS. PromptUnit cuts your LLM costs automatically with one line of code. Different tools, different jobs.

Helicone is an LLM observability proxy. PromptUnit is an LLM cost optimization layer. They answer different questions. Here is which one you actually need.

Langfuse is an open-source LLM observability and tracing platform. PromptUnit is a cost optimization proxy. They do different things and often belong together.

LangSmith is LangChain's evaluation and debugging platform for LLM apps. PromptUnit is a cost optimization proxy. Different problems, often used together.

Portkey and PromptUnit are both LLM gateways with routing and fallback. Portkey optimizes for control and reliability. PromptUnit optimizes for cost reduction.

OpenAI at $0.02/M, Voyage 4 at $0.06/M, Cohere Embed v4 at $0.12/M. How to route embedding calls by workload type to match cost to quality requirements.

GLM-5.1 scored 58.4 on SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6, at $0.95 per million input tokens. The coding routing default just changed.

Current SWE-bench scores and a cost-quality matrix for the best coding LLMs in 2026, from Claude Mythos to DeepSeek R2. Which model to use, and at what price.

A routing-first guide to the best LLM for coding in 2026. Which coding tasks go to which model, cost per task type, and SWE-bench benchmark scores compared.

Claude Haiku 4.5 costs $1/$5 per million tokens. Sonnet 4.6 costs $3/$15. Here is when Haiku matches Sonnet, when it fails, and routing logic for each task.

GPT-4o-mini costs $0.15/$0.60 per million tokens vs GPT-4o at $2.50/$10. Full pricing breakdown, cost comparison at scale, and which tasks justify the switch.

What metrics to track for LLM cost visibility, how to set up per-feature monitoring, how to catch cost spikes, and how to enforce budgets in production.

OpenRouter is a model marketplace. LiteLLM is an open-source proxy. PromptUnit is a cost optimization layer. Different jobs. Here is which to use for what.

An AI router directs LLM API calls to the optimal model by cost, quality, and task type. How it works, rule-based vs ML routing, and when you need one.

An LLM gateway is a control layer between your app and model providers. Here is how it differs from a proxy, SDK wrapper, and router, and when you need one.

The PromptUnit GitHub Action scans your PR diff for expensive models like GPT-4o and Claude Opus, then posts routing savings estimates before the code merges.

Tool use resends definitions on every turn and re-includes results in context. Agent loops compound the cost, and token bills run 3-5x over projections.

OpenAI, Anthropic, Google, and Groq offer 50% off batch processing for async workloads. Stack it with caching for 75-95% savings. Which tasks can wait?

Anthropic limited its strongest model to 50 partners at $25/$125 per million tokens. Here is why Project Glasswing pricing matters even if you cannot access it.

Gemini 2.5 Flash offers a 1M context window at $0.30/$2.50 per million tokens. Long-context vs. RAG routing depends on questions most teams never ask.

Gemma 4 31B hits 89.2% AIME at 855 t/s on one H100, under Apache 2.0. The self-hosting break-even against API providers just dropped. Here is the math.

GPT-5.5 ships omnimodal architecture at $5/$30 per million tokens. Routing all multimodal calls to one model is tempting. Here is when to split instead.

A B2B analytics team cut AI spend from $12,400 to $6,960 per month. 44% reduction, zero impact on output quality. How PromptUnit identified the savings.

DeepSeek R2 beats o3 on MATH, GPQA, and AIME at $0.07/$0.27 per million tokens. Defaulting to o3 for reasoning now costs a 30x premium for less accuracy.

Groq LPUs run Llama 3.1 8B at $0.05/$0.08 per million tokens and 1,000 t/s, 10x the throughput of GPT-4o at a tenth the cost. Which workloads should move?

DeepSeek V4 Pro hits frontier coding benchmarks at a third the price of Claude Opus 4.7, with MIT-licensed weights. Here is where it fits your routing stack.

GPT-5.5 launched at $5/$30 per million tokens, double GPT-5.4. Claude Opus 4.7 sits at $5/$25 with the SWE-Bench lead. Here's what changed for routing.

OpenAI's API, ChatGPT, and Codex went down on April 20, 2026 for hours. If your product broke, you have a single-provider problem. Here is the architecture fix.

GPT-5.4 mini scored 72.1% on OSWorld against the flagship's 75.0%, at 70% lower cost. Here is the benchmark-by-benchmark routing math after the March release.

How routing LLM traffic across OpenAI, Anthropic, and Google simultaneously reduces costs, improves reliability, and doesn't require compromising on quality.

A data-driven breakdown of real production LLM traffic showing which tasks actually require frontier models, and which are burning money unnecessarily.

A practical guide for engineering teams: which tasks GPT-4o-mini handles as well as GPT-4o at 16x lower output cost, and where that trade-off isn't worth it.

Beyond the API invoice: the real financial and operational cost of routing every LLM call to your most capable model, and the compounding effect over time.

A complete guide to LLM model routing: how it works, strategies that cut costs 40-85%, quality validation, and implementing it without a codebase rewrite.

Current OpenAI API pricing for all major models, a practical cost calculator, and strategies to reduce your bill by 40–70% using intelligent model selection.

Most engineering teams are overpaying for LLM API calls by 50–70%. Here's exactly how to fix it with five cost levers, without touching your application code.

A technical explainer on AI inference proxies, what they do, how they differ from gateways and SDKs, and when they make sense for production LLM systems.